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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare and process data	30–35%	- Ingest and transform data 1. Implement schema enforcement, schema drift, and slowly changing dimensions 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake 3. Ingest batch and streaming data from multiple sources - Optimize and manage data storage 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize 2. Handle structured, semi-structured, and unstructured data 3. Implement lakehouse architecture and manage table versions
Topic 2: Deploy and maintain data pipelines and workloads	30–35%	- Monitor, troubleshoot, and maintain workloads 1. Apply SDLC practices and version control 2. Monitor performance, logs, and execution metrics 3. Troubleshoot failures, repair and restart jobs - Build and orchestrate pipelines 1. Design and implement Lakeflow Spark Declarative Pipelines 2. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
Topic 3: Set up and configure an Azure Databricks environment	15–20%	- Integrate with Azure services 1. Configure monitoring with Azure Monitor and diagnostic settings 2. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID - Select and configure compute resources 1. Choose compute types: serverless, job compute, SQL warehouse, classic compute 2. Manage workspace settings, permissions, and networking 3. Configure cluster policies, instance pools, and libraries
Topic 4: Secure and govern Unity Catalog objects	15–20%	- Implement data governance and security 1. Enforce data quality, lineage, and auditing 2. Configure access control: row-level, column-level, attribute-based security 3. Manage catalogs, schemas, tables, views, and volumes - Manage data sharing and permissions 1. Set up external locations and storage credentials 2. Grant and revoke permissions, manage groups and service principals

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains:
* A catalog named Corpdb
* A schema named Finance in the Corpdb catalog
* A table named Sales in the Finance schema
You have a group named Analysts.
You assign the following permissions to Analysts:
* USE CATALOG on the Corpdb catalog
* USE SCHEMA on the Finance schema
* SELECT on the Sales table
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

2. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! contains three tasks named Task1, Task2. and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. You have an Azure Databricks workspace and a remote Git repository named Repo1. Repo1 contains two branches named main and Branch1.
You are on a development team that works in Repo1.
You commit changes to Branch1 and must merge the changes into main.
Before completing the merge, you need to meet the following requirements:
* Ensure that Branch1 includes the changes committed to main since Branch1 was created.
* Ensure that merge conflicts are detected and resolved.
What should you do first?

A) Pull the latest changes from Branch1.
B) Create a pull request for Branch1.
C) Pull the latest changes from main.
D) Merge main into Branch1.

4. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
* New data must be available in near-real-time (NRT).
* The data must be stored in managed Delta tables.
* The solution must minimize custom code and maintenance effort.
What should you include in the solution?

A) scheduled Apache Spark batch jobs
B) an Azure Data Factory pipeline
C) an external table that references the CSV files
D) Auto Loader

5. You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a Databricks access connector.
Does this meet the goal?

A) Yes
B) No

Solutions:

Question # 1
Answer: Only visible for members

Question # 2
Answer: Only visible for members

Question # 3
Answer: C

Question # 4
Answer: D

Question # 5
Answer: B